Long-term Dynamics and Peasant Autonomy in the Italian Countryside

8.1 Case studies

The following map shows the sites under investigation, divided by chronology. Please select the desired chronology (or chronologies) from the legend on the right.

Legend: R = Roman, LR = Late Roman, EMA = Early Middle Ages, Ma = 11th c. onwards

8.2 Medians

The faunal dataset is large (434+ records) and diversified. Looking at the distributions of each animal, the curve is not gaussian. The best choice for non-normal curves is to use medians instead of means to come up with figures that are less dependent on outliers. The function Medians_Chrono_Zoo() (Section 3.1) can be used to return as output weighted medians for each chronology. The in-depth description of how weights are calculated for each sample can be found in Section 6.4.1. To summarise, sites with a very large (i.e. fuzzy) chronology contribute less to the calculation of the median. Table 8.1 provides the median values of the main categories of faunal remains for each chronology, and Table 8.2 the median values for each century. Stronger colours in the cells indicate higher values.

Show the code

Medians_Categorised_per_Chronology_ZOO <- 
  data.frame(
    Medians_Chrono_Zoo(zooarch_cond, "R")*100,
    Medians_Chrono_Zoo(zooarch_cond, "LR")*100,
    Medians_Chrono_Zoo(zooarch_cond, "EMA")*100,
    Medians_Chrono_Zoo(zooarch_cond, "Ma")*100
  )

# Round to 2 digits
Medians_Categorised_per_Chronology_ZOO <- round(Medians_Categorised_per_Chronology_ZOO, 2)

## Weighted medians per century ##
Medians_ZOO_Centuries <- data.frame(
  "I BCE" = zooarch_tables(zooarch_cond, -1)$Medians,  
  "I CE" = zooarch_tables(zooarch_cond, 1)$Medians,
  "II CE" = zooarch_tables(zooarch_cond, 2)$Medians,
  "III CE" = zooarch_tables(zooarch_cond, 3)$Medians,
  "IV CE" = zooarch_tables(zooarch_cond, 4)$Medians,
  "V CE" = zooarch_tables(zooarch_cond, 5)$Medians,
  "VI CE" = zooarch_tables(zooarch_cond, 6)$Medians,
  "VII CE" = zooarch_tables(zooarch_cond, 7)$Medians,
  "VIII CE" = zooarch_tables(zooarch_cond, 8)$Medians,
  "IX CE" = zooarch_tables(zooarch_cond, 9)$Medians,
  "X CE" = zooarch_tables(zooarch_cond, 10)$Medians,
  "XI CE" = zooarch_tables(zooarch_cond, 11)$Medians
)

# Assigning the colnames (optional - instead of roman numerals)
colnames(Medians_ZOO_Centuries) <- c("1st c. BCE", "1st c. CE", "2nd c.", "3rd c.", "4th c.", "5th c.", "6th c.", "7th c.", "8th c.", "9th c.", "10th c.", "11th c.")

# Rounding the medians
Medians_ZOO_Centuries <- round(Medians_ZOO_Centuries, digits=2)

# Removing categories that are not necessary
Medians_ZOO_Centuries <- Medians_ZOO_Centuries[-c(6:9),]

Table 8.1: Weighted medians of zooarchaeological remains, divided by chronology.
	Chronologies
	R	LR	EMA	Ma
Pigs	48.0	38.62	35.00	35.62
Cattle	10.1	8.36	18.00	19.00
Caprine	25.0	22.08	29.00	27.00
Dom..Fowl	4.0	6.00	5.00	5.00
Edible.W..Mammals	5.0	3.00	3.00	3.00
Fish	1.0	2.00	3.93	1.00
Mollusca	11.0	8.00	4.00	2.89
Unedible.Dom..Mammals	2.0	3.00	3.95	2.00
Unedible.Wild.Mammals	1.0	1.00	1.00	1.00

Pigs’ medians from the Italian peninsula are the highest in each chronology, although their values decrease after the Roman age peak. Cattle medians slightly decrease after the Roman age, even though surprisingly (put a reference here to literature review to explain why surprisingly) the values increase again (18–19.71%) during the early Medieval and Medieval age. The trends for sheeps and goats are also interesting. During the Roman age the Italian median is 25%, slightly decreasing in the 3^rd to the 5^th century, and increasing again after. When discussing sheep-farming, one must always consider the geographical features from which the data is being collected. This will be discussed later on in the chapter, where more regional and geographical trends will be provided. Domestic fowl (chickens and geese) has quite stable values of 4-5%, with a peak of 7.68% in the 11^th century. Wild game peaks during the Roman age, with a median value of 5%, reaching a minimum in the early Middle ages (2%) and rising again in the 11^th century. Two considerations must be made for game consumption. The first is that as we will see later on, game consumption is strongly related to the site typology. Secondly, the Roman age value is pulled up by assemblages from the 1^st century BCE. After that, the values strongly decrease and by looking at the individual centuries the medians from the 7^th century onwards are much higher (ranging from 1.42% to 2.09%).

Table 8.2: Weighted medians of zooarchaeological remains, divided by century.
	Faunal remains
	Pigs	Cattle	Caprine	Domestic fowl	Wild game
1st c. BCE	39.70	11.41	26.07	0.00	0.76
1st c. CE	40.89	11.31	25.48	0.00	0.58
2nd c.	48.20	10.28	22.33	0.79	0.00
3rd c.	41.57	8.24	19.22	1.53	0.73
4th c.	34.15	9.56	23.23	1.79	0.98
5th c.	34.01	13.38	24.69	2.33	0.88
6th c.	31.83	20.66	28.55	2.75	0.65
7th c.	30.42	18.52	30.14	4.51	1.58
8th c.	33.63	13.89	30.13	2.70	1.18
9th c.	37.54	11.76	23.32	1.16	1.54
10th c.	35.77	14.36	25.64	1.63	1.93
11th c.	34.08	19.34	27.44	1.91	2.09
^* The color gradients in this table are used to indicate the chronologies.

8.2.1 Medians of faunal remains by context type

The weighted medians included below have been generated using the package dplyr and the summarize() function, applied to the exported relative proportion table (using the custom function zooarch_tables_general(), described in Section 3.2). The medians have been calculated for four animal categories (pigs, cattle, caprine, and game) for each site type and chronology. After, similar context types have been merged to simplify the reading; for example, the category Castle has been merged with the category Castrum, as they both indicate élite/military fortified contexts.

8.2.2 Medians of faunal remains by macro region

The process for generating weighted medians for the three Italian macro regions (Southern, Central and Northern Italy) has followed the same logic used in the previous section. The medians have been calculated for four animal categories (pigs, cattle, caprine, and game) for each macro area and chronology.

8.2.3 Medians of faunal remains by geography type

Weighted medians have been generated for the four geographies considered (plain, hill, hilltop, coast, and mountain), after the categories Hill and Hilltop have been merged. The medians have been calculated for four animal categories (pigs, cattle, caprine, and game) for each geography and chronology.

8.2.4 Caprine vs altitude

8.3 Future work to do

1. Animal sizes must be considered:

find a way
at least cattle
at least in the south

2. Feature selection models: Which feature is most discriminating the dataset?

E.g. Is the geographical context (Plain, Hill, …) more important than Southern vs Northern Italy?
It might be worth it, if data allows it, to subset the geographical features of Southern Italy or by main type (Rural vs Urban / Elite vs non-Elite)
Maybe soil type is not so useful for animals? Ask someone
What causes a different distribution of cattle in Italy in each chronology?
What are the factors that influence the most the different distribution of domestic animals?

8.4 Test 1: RDA + Anova

The RDA is performed on the condensed zooarchaeological export from the database, using the vegan package. The absolute counts are converted to frequencies (using the function decostand()).

The density of each animal % is shown in the plots below.

8.5 Test 2: GLM

What was it I needed to plot?

Loading required package: carData

Use the command
    lattice::trellis.par.set(effectsTheme())
  to customize lattice options for effects plots.
See ?effectsTheme for details.


Call:
glm(formula = Pigs ~ Type * Chronology, family = "quasibinomial", 
    data = Animals_Df, weights = Tot_NISP)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-31.1094   -4.1745   -0.1002    3.5311   30.5569  

Coefficients: (2 not defined because of singularities)
                                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)                          0.30878    0.17677   1.747 0.081271 .  
TypeRural                           -1.34116    0.21715  -6.176 1.34e-09 ***
TypeRural site, villa               -0.65401    0.21274  -3.074 0.002223 ** 
TypeUrban                           -0.40567    0.16103  -2.519 0.012066 *  
ChronologyLR                        -1.01980    0.22174  -4.599 5.35e-06 ***
ChronologyEMA                       -0.54845    0.19779  -2.773 0.005759 ** 
ChronologyMa                        -0.37379    0.14755  -2.533 0.011599 *  
TypeRural:ChronologyLR               1.03082    0.28043   3.676 0.000262 ***
TypeRural site, villa:ChronologyLR   1.59993    0.26853   5.958 4.75e-09 ***
TypeUrban:ChronologyLR               0.54478    0.22235   2.450 0.014613 *  
TypeRural:ChronologyEMA              0.64724    0.29000   2.232 0.026056 *  
TypeRural site, villa:ChronologyEMA  1.26023    0.26483   4.759 2.54e-06 ***
TypeUrban:ChronologyEMA             -0.02015    0.20238  -0.100 0.920729    
TypeRural:ChronologyMa               0.76581    0.33533   2.284 0.022793 *  
TypeRural site, villa:ChronologyMa        NA         NA      NA       NA    
TypeUrban:ChronologyMa                    NA         NA      NA       NA    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for quasibinomial family taken to be 53.31751)

    Null deviance: 40097  on 526  degrees of freedom
Residual deviance: 29221  on 513  degrees of freedom
  (6 observations deleted due to missingness)
AIC: NA

Number of Fisher Scoring iterations: 4

Pseudo R-squared:  0.2712432

Note:
  2 values in the Type*Chronology effect are not estimable